In [1]:
import pandas as pd
import numpy as np

from sklearn.cluster import KMeans
from sklearn import metrics

import seaborn as sns
import plotly as py
import plotly.graph_objs as go
import matplotlib.pyplot as plt
%matplotlib inline
In [2]:
df = pd.read_csv('Mall_Customers.csv')
In [3]:
df
Out[3]:
CustomerID Gender Age Annual Income (k$) Spending Score (1-100)
0 1 Male 19 15 39
1 2 Male 21 15 81
2 3 Female 20 16 6
3 4 Female 23 16 77
4 5 Female 31 17 40
... ... ... ... ... ...
195 196 Female 35 120 79
196 197 Female 45 126 28
197 198 Male 32 126 74
198 199 Male 32 137 18
199 200 Male 30 137 83

200 rows × 5 columns

In [4]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 200 entries, 0 to 199
Data columns (total 5 columns):
 #   Column                  Non-Null Count  Dtype 
---  ------                  --------------  ----- 
 0   CustomerID              200 non-null    int64 
 1   Gender                  200 non-null    object
 2   Age                     200 non-null    int64 
 3   Annual Income (k$)      200 non-null    int64 
 4   Spending Score (1-100)  200 non-null    int64 
dtypes: int64(4), object(1)
memory usage: 7.9+ KB

# Kiểm tra dữ liệu

In [5]:
total = df.isnull().sum().sort_values(ascending=False)
percent_1=df.isnull().sum()/df.isnull().count()*100
percent_2 = (round(percent_1,1)).sort_values(ascending = False)
missing_data = pd.concat([total,percent_2],axis=1,keys=['Total','%'])
missing_data.head(5)
Out[5]:
Total %
CustomerID 0 0.0
Gender 0 0.0
Age 0 0.0
Annual Income (k$) 0 0.0
Spending Score (1-100) 0 0.0

2. Sử dụng biểu đồ tần suất (Histogram) trong thư viện matplotlib để biểu diễn phân phối của lần lượt các thuộc tính Tuổi, Thu nhập hằng năm và Số điểm mua sắm.

In [6]:
plt.style.use('fivethirtyeight')

plt.figure(1,figsize =(15,6))
n = 0
for x in ['Age','Annual Income (k$)','Spending Score (1-100)']:
    n+=1
    plt.subplot(1,3,n)
    plt.subplots_adjust(hspace =0.5,wspace=0.5)
    sns.distplot(df[x],bins = 20)
    plt.title('Distplot of {}'.format(x))

plt.show()

C:\Users\GIA KIET\AppData\Local\Temp\ipykernel_11836\3015528420.py:9: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[x],bins = 20)
C:\Users\GIA KIET\AppData\Local\Temp\ipykernel_11836\3015528420.py:9: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[x],bins = 20)
C:\Users\GIA KIET\AppData\Local\Temp\ipykernel_11836\3015528420.py:9: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(df[x],bins = 20)
No description has been provided for this image

3. Thống kê số mẫu dữ liệu theo giới tính

In [7]:
plt.figure(1, figsize=(15,5))
sns.countplot(y='Gender', data=df)
plt.show()
No description has been provided for this image

4. Sử dụng biểu đồ phân tán (Scatter plot) để khảo sát phân bố của thuộc tính Tuổi và Thu nhập hằng năm theo Giới tính.

In [8]:
plt.figure(1, figsize = (15, 6))
for gender in ['Male', 'Female']:
    plt.scatter(x = 'Age', y = 'Annual Income (k$)', data = df[df['Gender'] == gender],
                s = 200, alpha = 0.5, label = gender)
plt.xlabel('Age'), plt.ylabel('Annual Income (k$)')
plt.title('Age vs Annual Income w.r.t Gender')
plt.legend()
plt.show()
No description has been provided for this image

5. Tương tự, sinh viên thực hiện vẽ biểu đồ khảo sát sự phân bố của thuộc tính Thu nhập hằng năm và Số điểm mua sắm theo Giới tính.

In [9]:
plt.figure(1, figsize = (15, 6))
for gender in ['Male', 'Female']:
    plt.scatter(x = 'Annual Income (k$)', y = 'Spending Score (1-100)', data = df[df['Gender'] == gender],
                s = 200, alpha = 0.5, label = gender)
plt.xlabel('Annual Income (k$)'), plt.ylabel('Spending Score (1-100)')
plt.title('Annual Income vs Spending Score w.r.t Gender')
plt.legend()
plt.show()
No description has been provided for this image

6.Sử dụng phương pháp khuỷu tay (ELBOW method) để xác định số cụm cần thiết.

In [10]:
X1 = df[['Age', 'Spending Score (1-100)']].iloc[: , :].values
inertia = []
for n in range(1, 11):
    algorithm = KMeans(n_clusters = n, init = 'random')
    algorithm.fit(X1)
    inertia.append(algorithm.inertia_)

D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(

7. Biểu diễn kết quả gom cụm bằng biểu đồ

In [11]:
plt.figure(1, figsize = (15, 6))
plt.plot(np.arange(1, 11), inertia, 'o')
plt.plot(np.arange(1, 11), inertia, '-', alpha = 0.5)
plt.xlabel('Number of Clusters'), plt.ylabel('Inertia')
plt.show()
No description has been provided for this image
In [12]:
algorithm = KMeans(n_clusters = 4, init = 'random')
algorithm.fit(X1)
labels1 = algorithm.labels_
centroids1 = algorithm.cluster_centers_

D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
In [13]:
plt.figure(1, figsize = (15, 7))
plt.clf()
plt.scatter(x = 'Age', y = 'Spending Score (1-100)', data = df, c = labels1, s = 200)
plt.scatter(x = centroids1[: , 0], y = centroids1[: , 1], s =300, c = 'red', alpha = 0.5)
plt.ylabel('Spending Score (1-100)'), plt.xlabel('Age')
plt.show()
No description has been provided for this image

8. Đánh giá kết quả gom cụm bằng Hệ số Dáng điệu (Silhouette coefficient)

In [14]:
metrics.silhouette_score(X1, labels1, metric = 'euclidean')
Out[14]:
0.49973941540141753
In [ ]:

9. Sinh viên thực hiện tương tự với trường hợp gom cụm theo các thuộc tính:

- Thu nhập hằng năm và Số điểm mua sắm.

- Tuổi, Thu nhập hằng năm và Số điểm mua sắm.

In [15]:
X2 = df[['Annual Income (k$)', 'Spending Score (1-100)']].iloc[: , :].values
inertia = []
for n in range(1, 11):
    algorithm = KMeans(n_clusters = n, init = 'random')
    algorithm.fit(X2)
    inertia.append(algorithm.inertia_)

D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
In [16]:
plt.figure(1, figsize = (15, 6))
plt.plot(np.arange(1, 11), inertia, 'o')
plt.plot(np.arange(1, 11), inertia, '-', alpha = 0.5)
plt.xlabel('Number of Clusters'), plt.ylabel('Inertia')
plt.show()
No description has been provided for this image
In [17]:
algorithm = KMeans(n_clusters = 5, init = 'random')
algorithm.fit(X2)
labels2 = algorithm.labels_
centroids2 = algorithm.cluster_centers_

D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
In [18]:
plt.figure(1, figsize = (15, 7))
plt.clf()
plt.scatter(x = 'Annual Income (k$)', y = 'Spending Score (1-100)', data = df, c = labels2, s = 200)
plt.scatter(x = centroids2[: , 0], y = centroids2[: , 1], s =300, c = 'red', alpha = 0.5)
plt.ylabel('Spending Score (1-100)'), plt.xlabel('Annual Income (k$)')
plt.show()
No description has been provided for this image
In [19]:
metrics.silhouette_score(X2, labels2, metric = 'euclidean')
Out[19]:
0.553931997444648
In [20]:
X3 = df[['Age','Annual Income (k$)', 'Spending Score (1-100)']].iloc[: , :].values
inertia = []
for n in range(1, 11):
    algorithm = KMeans(n_clusters = n, init = 'random')
    algorithm.fit(X3)
    inertia.append(algorithm.inertia_)

D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
In [21]:
plt.figure(1, figsize = (15, 6))
plt.plot(np.arange(1, 11), inertia, 'o')
plt.plot(np.arange(1, 11), inertia, '-', alpha = 0.5)
plt.xlabel('Number of Clusters'), plt.ylabel('Inertia')
plt.show()
No description has been provided for this image
In [22]:
algorithm = KMeans(n_clusters = 5, init = 'random')
algorithm.fit(X3)
labels3 = algorithm.labels_
centroids3 = algorithm.cluster_centers_

D:\APP\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
In [23]:
df['label3'] = labels3
trace1 = go.Scatter3d(
    x = df['Age'],
    y = df['Spending Score (1-100)'],
    z = df['Annual Income (k$)'],
    mode = 'markers',
    marker = dict(
        color = df['label3'],
        size = 20,
        line = dict(
            color = df['label3'],
            width = 12
        ),
        opacity = 0.8
    )
)
dat = [trace1]
layout = go.Layout(
    title = 'Clusters',
    scene = dict(
        xaxis = dict(title = 'Age'),
        yaxis = dict(title = 'Spending Score'),
        zaxis = dict(title = 'Annual Income')
    )
)
fig = go.Figure(data = dat, layout = layout)
py.offline.iplot(fig)